Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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In: 10.1007/s00170-020-06511-3
- Event
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Veröffentlichung
- (where)
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Aachen
- (who)
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Universitätsbibliothek der RWTH Aachen
- (when)
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2021
- Creator
- DOI
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10.18154/RWTH-2021-01831
- URN
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urn:nbn:de:101:1-2021021702483782078692
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:33 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Lockner, Yannik
- Hopmann, Christian
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2021
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Analyse des Keramikspritzverfahrens zur Herstellung kompakter Bauteile und unter Einsatz der Gasinjektionstechnik : = Analysis of ceramical injection moulding for the production of compact parts as well as using gas injection technique
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Prediction and validation of the specific volume for inline warpage control in injection molding
Comparison of hybrid machine learning approaches for surrogate modeling part shrinkage in injection molding
Measurement of specific volume of polymers under simulated injection molding processes
Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks
Foundations for Enabling a Smart Injection Molding Factory
Induced netword-based transfer learning in injection molding for process modelling and optimization with artificial neural networks
Injection molding of semi‐crystalline polymers in a space‐time framework
Analyse des Keramikspritzverfahrens zur Herstellung kompakter Bauteile und unter Einsatz der Gasinjektionstechnik : = Analysis of ceramical injection moulding for the production of compact parts as well as using gas injection technique
Towards an ontology-based dictionary for production planning and control in the domain of injection molding as a basis for standardized asset administration shells
Injection molding of semi-crystalline polymers in a space-time framework
Evaluation of healing models to predict the weld line strength of the amorphous thermoplastic polystyrene by injection molding simulation
High‐resolution numerical simulations of polymer injection molding: Analysis of mesh size and refinement
Prediction and validation of the specific volume for inline warpage control in injection molding
Comparison of hybrid machine learning approaches for surrogate modeling part shrinkage in injection molding
Measurement of specific volume of polymers under simulated injection molding processes
Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks
Foundations for Enabling a Smart Injection Molding Factory
Induced netword-based transfer learning in injection molding for process modelling and optimization with artificial neural networks
Injection molding of semi‐crystalline polymers in a space‐time framework
Analyse des Keramikspritzverfahrens zur Herstellung kompakter Bauteile und unter Einsatz der Gasinjektionstechnik : = Analysis of ceramical injection moulding for the production of compact parts as well as using gas injection technique
Towards an ontology-based dictionary for production planning and control in the domain of injection molding as a basis for standardized asset administration shells
Injection molding of semi-crystalline polymers in a space-time framework
Evaluation of healing models to predict the weld line strength of the amorphous thermoplastic polystyrene by injection molding simulation
High‐resolution numerical simulations of polymer injection molding: Analysis of mesh size and refinement
Prediction and validation of the specific volume for inline warpage control in injection molding
Comparison of hybrid machine learning approaches for surrogate modeling part shrinkage in injection molding